Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.

As a Lead Data Engineer at JPMorgan Chase within the CCB (Connected Commerce), you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

Architect and oversee the design of complex data solutions that meet diverse business needs and customer requirements. Guide the evolution of logical and physical data models to support emerging business use cases and technological advancements. Build and manage end-to-end cloud-native data pipelines in AWS, leveraging your hands-on expertise with AWS components. Build analytical systems from the ground up, providing architectural direction, translating business issues into specific requirements, and identifying appropriate data to support solutions. Work across the Service Delivery Lifecycle on engineering major/minor enhancements and ongoing maintenance of existing applications. Conduct feasibility studies, capacity planning, and process redesign/re-engineering of complex integration solutions. Help others build code to extract raw data, coach the team on techniques to validate its quality, and apply your deep data knowledge to ensure the correct data is ingested across the pipeline. Guide the development of data tools used to transform, manage, and access data, and advise the team on writing and validating code to test the storage and availability of data platforms for resilience. Oversee the implementation of performance monitoring protocols across data pipelines, coaching the team on building visualizations and aggregations to monitor pipeline health. Coach others on implementing solutions and self-healing processes that minimize points of failure across multiple product features.

 

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience  Extensive experience in managing the full lifecycle of data, from collection and storage to analysis and reporting. Proficiency in one or more large-scale data processing distributions such as JavaSpark along with knowledge on Data Pipeline (DPL), Data Modeling, Data warehouse, Data Migration and so-on. Hands-on practical experience in system design, application development, testing, and operational stability Proficient in coding in one or more modern programming languages Should have good hands-on experience on AWS services and its components along with good understanding on Kubernetes. Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages. Strong understanding of domain driven design, micro-services patterns, and architecture Overall knowledge of the Software Development Life Cycle along with experience with IBM MQ, Apache Kafka Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, LLMs etc.)

 

Preferred qualifications, capabilities, and skills   Familiarity with modern front-end technologies Experience designing and building REST API services using Java Exposure to cloud technologies - knowledge on Hybrid cloud architectures is highly desirable.